feat(ts-sdk): add FastEmbed embedding provider (#5862)

Co-authored-by: kartik-mem0 <kartik.labhshetwar@mem0.ai>

docs/components/embedders/models/fastembed.mdx

@@ -7,10 +7,18 @@ You can use FastEmbed to run embedding models locally in Mem0. FastEmbed is an O
 
 ### Installation
 
-```bash
+FastEmbed is an optional dependency, so install it alongside Mem0.
+
+<CodeGroup>
+```bash Python
 pip install fastembed
 ```
 
+```bash TypeScript
+npm install fastembed
+```
+</CodeGroup>
+
 ### Usage
 
 <CodeGroup>
@@ -38,13 +46,66 @@ messages = [
 ]
 m.add(messages, user_id="john")
 ```
+
+```typescript TypeScript
+import { Memory } from "mem0ai/oss";
+
+// FastEmbed needs no API key. Leave the embedder config empty to use the
+// default model (fast-bge-small-en-v1.5), or set `model` to one of the
+// supported models listed below.
+const memory = new Memory({
+  embedder: {
+    provider: "fastembed",
+    config: {
+      model: "fast-bge-small-en-v1.5",
+    },
+  },
+  llm: {
+    provider: "openai",
+    config: { apiKey: process.env.OPENAI_API_KEY }, // For fact extraction
+  },
+});
+
+const messages = [
+  { role: "user", content: "I'm planning to watch a movie tonight. Any recommendations?" },
+  { role: "assistant", content: "How about thriller movies? They can be quite engaging." },
+  { role: "user", content: "I'm not a big fan of thriller movies but I love sci-fi movies." },
+  { role: "assistant", content: "Got it! I'll avoid thriller recommendations and suggest sci-fi movies in the future." },
+];
+await memory.add(messages, { userId: "john" });
+```
 </CodeGroup>
 
+<Note>
+  **The Python and TypeScript SDKs default to different models.** Python defaults to `thenlper/gte-large` (1024 dimensions), while TypeScript defaults to `fast-bge-small-en-v1.5` (384 dimensions). The TypeScript package (`fastembed` on npm) ships a fixed set of ONNX models and does not include `thenlper/gte-large`. Because the two defaults produce vectors of different dimensions, do not point both SDKs at the same vector store collection unless you configure them to use the same model.
+</Note>
+
+The TypeScript SDK supports these FastEmbed models. Pass the exact string as `model`:
+
+- `fast-bge-small-en-v1.5` (default)
+- `fast-bge-small-en`
+- `fast-bge-base-en`
+- `fast-bge-base-en-v1.5`
+- `fast-bge-small-zh-v1.5`
+- `fast-all-MiniLM-L6-v2`
+- `fast-multilingual-e5-large`
+
 ### Config
 
-Here are the parameters available for configuring FastEmbed embedder:
+Here are the parameters available for configuring the FastEmbed embedder:
 
+<Tabs>
+<Tab title="Python">
 | Parameter | Description | Default Value |
 | --- | --- | --- |
 | `model` | The name of the FastEmbed model to use | `thenlper/gte-large` |
 | `embedding_dims` | Dimensions of the embedding model (auto-derived from the model if not set) | `None` |
+</Tab>
+<Tab title="TypeScript">
+| Parameter | Description | Default Value |
+| --- | --- | --- |
+| `model` | The FastEmbed model to use (see the supported list above) | `fast-bge-small-en-v1.5` |
+
+The embedding dimension is detected automatically at startup, so you do not need to set it manually.
+</Tab>
+</Tabs>

docs/components/embedders/overview.mdx

@@ -10,7 +10,7 @@ Mem0 offers support for various embedding models, allowing users to choose the o
 See the list of supported embedders below.
 
 <Note>
-  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **Google AI**, **Langchain**, **LM Studio**, **Ollama**, and **Together**.
+  All embedders listed below are supported in the Python implementation. The TypeScript implementation supports: **OpenAI**, **Azure OpenAI**, **FastEmbed**, **Google AI**, **Langchain**, **LM Studio**, **Ollama**, and **Together**.
 </Note>
 
 <CardGroup cols={4}>